

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor




class   UpperCamelCase			(  unittest.TestCase ):

    def __init__(      self			:   List[Any]				,     snake_case__			:   Dict				,     snake_case__			:   List[str]=7				,     snake_case__			:   List[Any]=3				,     snake_case__			:   Optional[Any]=1_8				,     snake_case__			:   List[str]=3_0				,     snake_case__			:   str=4_0_0				,     snake_case__			:   List[str]=True				,     snake_case__			:   Tuple=None				,     snake_case__			:   str=True				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      size if size is not None else {"""height""": 1_8, """width""": 1_8}
     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      batch_size
     SCREAMING_SNAKE_CASE		    =      num_channels
     SCREAMING_SNAKE_CASE		    =      image_size
     SCREAMING_SNAKE_CASE		    =      min_resolution
     SCREAMING_SNAKE_CASE		    =      max_resolution
     SCREAMING_SNAKE_CASE		    =      do_resize
     SCREAMING_SNAKE_CASE		    =      size
     SCREAMING_SNAKE_CASE		    =      do_normalize




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]     ),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }




@require_torch
@require_vision
class   UpperCamelCase			(  a_						,      unittest.TestCase ):
    __UpperCamelCase    =ImageGPTImageProcessor if is_vision_available() else None

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ImageGPTImageProcessingTester(self     )

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(_lowercase				,     'clusters'     )     )
     self.assertTrue(hasattr(_lowercase				,     'do_resize'     )     )
     self.assertTrue(hasattr(_lowercase				,     'size'     )     )
     self.assertTrue(hasattr(_lowercase				,     'do_normalize'     )     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class.from_dict(self.image_processor_dict     )
     self.assertEqual(image_processor.size				,     {'height': 1_8, 'width': 1_8}     )

     SCREAMING_SNAKE_CASE		    =      self.image_processing_class.from_dict(self.image_processor_dict				,     size=4_2     )
     self.assertEqual(image_processor.size				,     {'height': 4_2, 'width': 4_2}     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(**self.image_processor_dict     )
     SCREAMING_SNAKE_CASE		    =      json.loads(image_processor.to_json_string()     )
     for key, value in self.image_processor_dict.items():
      if key == "clusters":
       self.assertTrue(np.array_equal(_lowercase				,     obj[key]     )     )
      else:
       self.assertEqual(obj[key]				,     _lowercase     )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(**self.image_processor_dict     )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE		    =      os.path.join(_lowercase				,     'image_processor.json'     )
      image_processor_first.to_json_file(_lowercase     )
      SCREAMING_SNAKE_CASE		    =      self.image_processing_class.from_json_file(_lowercase     ).to_dict()

     SCREAMING_SNAKE_CASE		    =      image_processor_first.to_dict()
     for key, value in image_processor_first.items():
      if key == "clusters":
       self.assertTrue(np.array_equal(_lowercase				,     image_processor_second[key]     )     )
      else:
       self.assertEqual(image_processor_first[key]				,     _lowercase     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.image_processing_class(**self.image_processor_dict     )

     with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(_lowercase     )
      SCREAMING_SNAKE_CASE		    =      self.image_processing_class.from_pretrained(_lowercase     ).to_dict()

     SCREAMING_SNAKE_CASE		    =      image_processor_first.to_dict()
     for key, value in image_processor_first.items():
      if key == "clusters":
       self.assertTrue(np.array_equal(_lowercase				,     image_processor_second[key]     )     )
      else:
       self.assertEqual(image_processor_first[key]				,     _lowercase     )




    @unittest.skip('ImageGPT requires clusters at initialization'     )
    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     pass


def 							__lowerCAmelCase     (							)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      load_dataset('hf-internal-testing/fixtures_image_utils'		,				split='test'						)

 SCREAMING_SNAKE_CASE		    =      Image.open(dataset[4]['file']						)
 SCREAMING_SNAKE_CASE		    =      Image.open(dataset[5]['file']						)

 SCREAMING_SNAKE_CASE		    =      [imagea, imagea]

 return images




@require_vision
@require_torch
class   UpperCamelCase			(  unittest.TestCase ):

    @slow
    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'     )

     SCREAMING_SNAKE_CASE		    =      prepare_images()

     # test non-batched
     SCREAMING_SNAKE_CASE		    =      image_processing(images[0]				,     return_tensors='pt'     )

     self.assertIsInstance(encoding.input_ids				,     torch.LongTensor     )
     self.assertEqual(encoding.input_ids.shape				,     (1, 1_0_2_4)     )

     SCREAMING_SNAKE_CASE		    =      [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()				,     _lowercase     )

     # test batched
     SCREAMING_SNAKE_CASE		    =      image_processing(_lowercase				,     return_tensors='pt'     )

     self.assertIsInstance(encoding.input_ids				,     torch.LongTensor     )
     self.assertEqual(encoding.input_ids.shape				,     (2, 1_0_2_4)     )

     SCREAMING_SNAKE_CASE		    =      [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     _lowercase     )




from __future__ import annotations


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= 0
			a_						:						str 							= len(a__) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									a_						:						List[Any] 							= i + 1
						else:
									a_						:						Optional[Any] 							= j - 1

			return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")


